关键词: 模糊C均值聚类, 空间距离, 鲁棒性

Abstract: To deal with the traditional image segmentation algorithms’ sensitive to noises and outliers, and the segmenting time increasing with the image size, the fast and Fuzzy C-Means clustering algorithm based on the space distance of nearest neighbors（SFGFCM） uses a nuclear space distance formula to calculate the similarity measure [Sij]between the query pixel and its nearest neighbors, further more to calculate the local information constrains image with the adjacent pixels weighted sum, at last sum the local information image’s grey level to cluster. So that it well balances the query pixel’s gray levels and local information, provides robustness to noisy images and guaranteed image details presentation, improves the accuracy of clustering, at the same time reduces the clustering time remarkably for the large size images. Experiments performed on a large number of synthetic and real-world images show that SFGFCM is more effective and efficient in contrast to traditional algorithm on image segmentation.

Key words: Fuzzy C-Means clustering, space distance, robustness